Automatic segmentation of MR brain images with a convolutional neural network (1704.03295v1)

Abstract: Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

• The paper introduces a multi-scale CNN that learns from patches of varying sizes to capture both fine details and broader spatial context in MR brain segmentation.
• It achieves robust performance with average Dice coefficients ranging from 0.82 to 0.91 across diverse datasets including preterm infants and adults.
• The study demonstrates the potential of deep learning in automating quantitative brain analysis, offering a scalable tool for neurodevelopment and ageing research.

Automatic Segmentation of MR Brain Images with a Convolutional Neural Network

The paper "Automatic Segmentation of MR Brain Images with a Convolutional Neural Network" by Pim Moeskops et al. presents a methodical approach to the automatic segmentation of MR brain images using Convolutional Neural Networks (CNNs). This method addresses a critical challenge in the quantitative analysis of brain images, enabling the processing of MR images across different ages and acquisition protocols.

Methodology

The proposed method leverages a CNN architecture designed to handle multi-scale information by incorporating multiple patch sizes and convolution kernel sizes. This is implemented to enhance the ability of the network to capture both fine details and spatial consistency in the segmentation process. Unlike traditional methods that rely heavily on manually defined features, this approach utilizes the capability of CNNs to learn relevant features from the training data.

In the training phase, the network handles patches of 25 × 25, 51 × 51, and 75 × 75 voxels. Each patch size corresponds to a specific set of convolution layers tailored to extract information at different scales. This multi-scale approach effectively balances local texture detail with broader spatial context, facilitating accurate voxel classification.

Evaluation & Results

The segmentation method was evaluated across five different datasets:

1. Coronal T2-weighted images of preterm infants at 30 weeks PMA.
2. Coronal T2-weighted images of preterm infants at 40 weeks PMA.
3. Axial T2-weighted images of preterm infants at 40 weeks PMA.
4. Axial T1-weighted images of ageing adults around 70 years old.
5. T1-weighted images of young adults around 23 years old.

The CNN achieved average Dice coefficients of 0.87, 0.82, 0.84, 0.86, and 0.91 across these datasets respectively, illustrating its robustness despite variations in age and imaging protocols.

Implications and Future Directions

The implications of this research are significant for both practical and theoretical applications in medical imaging. Practically, the methodology enables high-throughput analysis of MR images, facilitating large-scale studies such as neurodevelopmental and ageing research. Theoretically, it demonstrates the efficacy of multi-scale CNNs in medical image analysis, paving the way for further explorations in other anatomical structures or imaging modalities.

Potential future developments could include:

• Extending the network's capability to include more diverse training datasets, thus enhancing its generalizability across different MR acquisition settings.
• Exploring the integration of orthogonal or 3D patches for an improved 3-dimensional context in isotropic images.
• Refining the network architecture to potentially increase the model’s performance with larger annotated datasets.

Conclusion

The method presented in this paper leverages the power of CNNs for the automatic segmentation of MR brain images, yielding accurate and consistent segmentation results across different age groups and imaging protocols. By addressing existing limitations in the manual definition of features and the application of multi-scale information, this work contributes a robust and adaptable tool for the field of medical image analysis.